Sensors that provide accurate and reliable measurements are crucial in today's world of highly automated and integrated operations. Sensor failure is one of the biggest nightmares of an operator. A sensor failure is defined as the loss of proper sensing action, that is, the sensor fails to respond correctly to changes in the measurement variable it is sensing. Often a sensor failure goes undetected until it escalates into a process problem. It may then be too late to take any preventive action. The process industry is filled with real-life case studies, in which a catastrophic incident can be traced back to a failed sensor.
A differential pressure cell (DP-cell) is commonly used to measure process variables like flow and level. The DP-cell is often located close to the ground for ease of maintenance and, hence, is connected to the process through long impulse lines. These impulse lines are easily blocked by accumulation of suspended particles in the process fluid, or by uneven steam flow during tracing, or by poor insulation. As used herein, the term “frozen sensor” is used to indicate the state of a sensor (either flow or level) that contains a blocked DP-cell. A blocked DP-cell measures incorrect pressure and, thus, provides an incorrect indication of flow or level. Unlike the failure of a temperature sensor that results in the measurement reading either pegged at a constant value or widely oscillating between lower and upper limits of a range of the instrument, a frozen sensor is more subtle to detect.
Traditionally, there are two ways of detecting a frozen sensor. First, an operator may suspect a frozen sensor based on his or her experience. Second, a spectral analysis may be performed to monitor changes in the high-frequency component of a measurement signal. Pressure p(t) described at a molecular level measures the net energy transferred by random impact of atoms and molecules at any point. The pressure p(t) at any time t is given by:p(t)={overscore (p)}(t)+p′(t)  (1)where, p′(t)represents fluctuations introduced because of turbulence and electrical interference and {overscore (p)}(t) represents the ensemble average of the instantaneous pressure calculated over a very small measurement volume. These fluctuating components of a measurement signal can often provide valuable insights into the state of the measuring device. Although the use of spectral analysis to monitor these fluctuations and deduce diagnostic states has been successful in laboratory setups, most methods require measurement frequencies in the range of 200-1000 Hz, which is rarely practical in process industries.
Thus, there is a need for an automated and predictive plugging detection system and method.